code: def nvidia_waveglow_vocode(mel_specgram, device, jit=False):
      waveglow = torch.hub.load(
          "NVIDIA/DeepLearningExamples:torchhub",
          "nvidia_waveglow",
          model_math="fp16",
      )
      waveglow = waveglow.remove_weightnorm(waveglow)
      waveglow = waveglow.to(device)
      waveglow.eval()
  
      if args.jit:
          raise ValueError(
              "Vocoder option `nvidia_waveglow is not jittable."
          )
  
      with torch.no_grad():
          waveform = waveglow.infer(mel_specgram).cpu()
  
      return waveform
  
file_path: audio/examples/pipeline_tacotron2/inference.py
imports: import argparse
  import os
  import random
  import sys
  from functools import partial
  import numpy
  import torch
  import torchaudio
  from datasets import InverseSpectralNormalization
  from text.text_preprocessing import available_phonemizers, available_symbol_set, get_symbol_list, text_to_sequence
  from torchaudio.models import Tacotron2, tacotron2
  from utils import prepare_input_sequence
label_desc: Use image node_id 3 for calling a global function with example usage: nvidia_waveglow_vocode(mel_specgram, device, jit) and returns: waveform